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Compute information-filter likelihoods in measurement space - #345
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InformationFilter.updatepasses the posterior precision matrix in state space to the innovation log-density. When state and observation dimensions differ, this can raise a broadcasting error; even equal dimensions give the wrong likelihood. Related to the covariance concern in #143.Compute the innovation covariance
H @ inv(P_inv) @ H.T + inv(R_inv)for the likelihood. Keep the existing information-form gain/state updates andSrepresentation unchanged. The extra inversions occur only when likelihood computation is enabled, preserving the no-statistics path for a singular prior.Tests compare state, posterior covariance, log-likelihood and likelihood against
KalmanFilterwith fewer/equal/more observations than state dimensions, plus a singular-prior update with likelihood disabled.Validation: 3 regression cases fail on unchanged main, and all 4 pass after the fix. Including existing information-filter tests: 7 passed (Python 3.11 / NumPy 1.26).
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